started: AL26Apr2019
last updated: AL30Aug2019

Summary

Inputs:
- WECARE-NFE only outliers: based on 1,634 variants x 715 samples ( 519BC = 258UBC + 260CBC and 197NFE)
- Joined WECARE-NFE-KGEN eigenvectors: 3,219 samples = 715 wecare-nfe + 2,504 kgen

Look where the WECARE-NFE only outliers are positioned in PCA plot based on the joined eigenvectors.
Surprisingly, cases of eastern asia ancestry have not been picked up by WECARE-NFE PCA.
So, a solution may be to exclude them manually.

Start_section

Sys.time()
## [1] "2019-08-30 18:19:46 BST"
rm(list=ls())
graphics.off()

library(knitr)
## Warning: package 'knitr' was built under R version 3.5.2
library(dplyr)
## Warning: package 'dplyr' was built under R version 3.5.2
## 
## Attaching package: 'dplyr'
## The following objects are masked from 'package:stats':
## 
##     filter, lag
## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union
library(ggplot2)
## Warning: package 'ggplot2' was built under R version 3.5.2
library(plotly)
## 
## Attaching package: 'plotly'
## The following object is masked from 'package:ggplot2':
## 
##     last_plot
## The following object is masked from 'package:stats':
## 
##     filter
## The following object is masked from 'package:graphics':
## 
##     layout
base_folder="/Users/alexey/Documents/wecare/ampliseq/v04_ampliseq_nfe/s15_add_PCs_exclude_outliers"

opts_knit$set(root.dir = base_folder)

options(stringsAsFactors = F)
options(warnPartialMatchArgs = T, 
        warnPartialMatchAttr = T, 
        warnPartialMatchDollar = T)

#options(error = browser()) # Type Q or c to exit, drop browser level
# https://support.rstudio.com/hc/en-us/articles/200713843?version=1.1.456&mode=desktop
# https://stackoverflow.com/questions/13052522/how-to-leave-the-r-browser-mode-in-the-console-window/13052588 

Read_data

# Sequencing data (for wecare phenotypes: case/control status)
source_folder="/Users/alexey/Documents/wecare/ampliseq/v04_ampliseq_nfe/s12_check_BRCA1_BRCA2_PALB2"
load(paste(source_folder, "s03_exclude_BRCA1_BCRA2_PALB2_carriers.RData", sep="/"))
base_folder="/Users/alexey/Documents/wecare/ampliseq/v04_ampliseq_nfe/s15_add_PCs_exclude_outliers"

source_folder="/Users/alexey/Documents/wecare/ampliseq/v04_ampliseq_nfe/s14_explore_wecare_1kg_PCA_plots/s03_all_variants_not_in_LD_971"

# Eigenvectors
eigenvectors_file <- paste(source_folder, "ampliseq_1kg_971_3219_100PCs.eigenvec", sep="/")
eigenvectors.df <- read.table(eigenvectors_file, header=T, sep="\t",quote="")

# 1kg phenotypes (ethnicity and gender)
kg_phenotypes_file <- paste(source_folder, "integrated_call_samples_v3.20130502.ALL.panel", sep="/")
kg_phenotypes.df <- read.table(kg_phenotypes_file, header=T)

# Clean-up
rm(source_folder, eigenvectors_file, kg_phenotypes_file, genotypes.mx, variants.df)

Check data

# List of objects
ls()
## [1] "base_folder"      "eigenvectors.df"  "kg_phenotypes.df" "phenotypes.df"
# Expected number of samples in eigenvectors
2504+715
## [1] 3219
# Dimentions of objects
dim(eigenvectors.df)
## [1] 3219  102
dim(kg_phenotypes.df)
## [1] 2504    4
dim(phenotypes.df)
## [1] 715  24
# Update eigenvectors
rownames(eigenvectors.df) <- eigenvectors.df$FID
eigenvectors.df <- eigenvectors.df[,-1]

Remove re-processed NFE samples, merge eigenvectors and phenotypes

# Make a table with IDs of overlapping NFE
eigenvectors.df[c(3022,3023),c("IID","PC1")]
##                     IID         PC1
## 9_S346_L008 9_S346_L008 -0.01348900
## 2:HG00097     2:HG00097 -0.00844256
nfe_pca <- eigenvectors.df$IID[3023:3219] # re-processed NFE added to Ampliseq
nfe_ampliseq <- sub("2:","",nfe_pca)
nfe.df <- data.frame(nfe_ampliseq, nfe_pca)

# Remove overlapping NFE from ampliseq-kgen eigenvectors
selected_samples <- ! eigenvectors.df$IID %in% nfe.df$nfe_pca
sum(selected_samples)
## [1] 3022
518+2504
## [1] 3022
eigenvectors_ampliseq_kgen.df <- eigenvectors.df[selected_samples,1:6]
"sample" -> colnames(eigenvectors_ampliseq_kgen.df)[1]

# Prepare ampliseq phenotypes 
# (for clarity of PCA plot wecare samples and controls were not separated)
phenotypes.df[c(518:519),c(1,2)]
##                long_ids illumina_id
## 9_S346_L008 9_S346_L008        S346
## HG00097         HG00097        <NA>
phenotypes_ampliseq.df <- phenotypes.df[1:518,c("long_ids","cc")]
table(phenotypes_ampliseq.df$cc)
## 
##   0   1 
## 258 260
"WECARE" -> phenotypes_ampliseq.df$cc[phenotypes_ampliseq.df$cc==1] # This could be named as CBC
"WECARE" -> phenotypes_ampliseq.df$cc[phenotypes_ampliseq.df$cc==0] # This could be named as UBC
table(phenotypes_ampliseq.df$cc)
## 
## WECARE 
##    518
c("sample","group") -> colnames(phenotypes_ampliseq.df)

# Prepare kgen phenotypes
phenotypes_kgen.df <- kg_phenotypes.df[,c("sample","super_pop")]
c("sample","group") -> colnames(phenotypes_kgen.df)

# Merge kgen and Ampliseq phenotypes (latest will be on top in the plot)
phenotypes_ampliseq_kgen.df <- rbind(phenotypes_kgen.df,phenotypes_ampliseq.df)
table(phenotypes_ampliseq_kgen.df$group)
## 
##    AFR    AMR    EAS    EUR    SAS WECARE 
##    661    347    504    503    489    518
# Add eigenvectors to phenotypes
dim(phenotypes_ampliseq_kgen.df)
## [1] 3022    2
dim(eigenvectors_ampliseq_kgen.df)
## [1] 3022    6
eigenphen_ampliseq_kgen.df <- full_join(
  phenotypes_ampliseq_kgen.df, eigenvectors_ampliseq_kgen.df, by="sample")
dim(eigenphen_ampliseq_kgen.df)
## [1] 3022    7
head(eigenphen_ampliseq_kgen.df)
##    sample group         PC1         PC2         PC3          PC4         PC5
## 1 HG00096   EUR -0.01258860 -0.01642160 0.000925435 -0.001809850  0.00780321
## 2 HG00097   EUR -0.00802238 -0.00726931 0.003157220 -0.002825920  0.00838845
## 3 HG00099   EUR -0.01012120 -0.01259730 0.002215210 -0.006756090  0.03568230
## 4 HG00100   EUR -0.00849754 -0.01684940 0.002751330 -0.001556380  0.01011790
## 5 HG00101   EUR -0.01243290 -0.00932382 0.000159626 -0.005677620  0.00907268
## 6 HG00102   EUR -0.00827301 -0.00616160 0.001818280  0.000230261 -0.00955764
tail(eigenphen_ampliseq_kgen.df)
##            sample  group        PC1          PC2          PC3         PC4          PC5
## 3017 95_S517_L008 WECARE -0.0125655  0.000696678  0.000508483  0.00301086  0.000758076
## 3018 96_S236_L007 WECARE -0.0103028 -0.009400760  0.000408240 -0.00126134  0.012597400
## 3019 97_S509_L008 WECARE -0.0116830 -0.016781000  0.003277310 -0.00472534  0.025380700
## 3020 98_S335_L008 WECARE -0.0108617 -0.011973100 -0.000350267 -0.00303147  0.006616230
## 3021 99_S418_L008 WECARE -0.0124556 -0.014528400 -0.002571390 -0.00271848  0.005434640
## 3022  9_S346_L008 WECARE -0.0134890 -0.012876200  0.004023890  0.00388655 -0.003655670
# Clean-up
rm(nfe_pca, nfe_ampliseq, selected_samples, eigenvectors_ampliseq_kgen.df, 
   phenotypes_ampliseq.df, phenotypes_ampliseq_kgen.df, nfe.df, phenotypes_kgen.df, 
   eigenvectors.df, kg_phenotypes.df, phenotypes.df)

Read data about PC outliers

# Read data with wecare-only PCa and outliers
load(paste(base_folder, "s01_add_PCs_1634_715.RData", sep="/"))

# Extract data about previously selected outliers (based on wecare only PCs: mean +/- 3sd)
outliers.df <- phenotypes.df %>% 
  filter(pc_outlier) %>% 
  select(long_ids)

wecare_pc_outlier <- eigenphen_ampliseq_kgen.df$sample %in% outliers.df$long_ids
sum(wecare_pc_outlier)
## [1] 19
# Add column with outliers to the main table
eigenphen_ampliseq_kgen.df <- data.frame(eigenphen_ampliseq_kgen.df, wecare_pc_outlier)

# Clean up
rm(genotypes.mx, phenotypes.df, variants.df, outliers.df, wecare_pc_outlier)

Make PCA plot

http://www.sthda.com/english/wiki/ggplot2-point-shapes

# Set outliers thresholds (manually selected on the basis of visual assessment of plots)
pc1_th <- 0.005
pc2_th <- 0.01

# Prepare vector fr colour scale
myColours <- c("EUR"="BLUE", "AFR"="YELLOW", "AMR"="GREEN",
               "SAS"="GREY", "EAS"="PINK", 
               "WECARE"="RED")

myColourScale <- scale_colour_manual(values=myColours)

# Static plot
ggplot(eigenphen_ampliseq_kgen.df, aes(PC1,PC2)) + 
  geom_point(aes(col=group, shape=wecare_pc_outlier)) +
  labs(title="PC1 vs PC2", x="PC1", y="PC2") +
  scale_shape_manual(values=c(16,4)) +
  geom_vline(xintercept=pc1_th, linetype="dashed", size=0.5) +
  geom_hline(yintercept=pc2_th, linetype="dashed", size=0.5) +
  myColourScale

# Interactive plot
plotly_group <- factor(eigenphen_ampliseq_kgen.df$group,
  levels=c("AFR","AMR","EAS","SAS","EUR","WECARE"))

g <- ggplot(eigenphen_ampliseq_kgen.df, aes(PC1,PC2)) + 
  geom_point(aes(col=plotly_group, shape=wecare_pc_outlier, text=sample)) +
  labs(title="PC1 vs PC2 (manual thresholds)", x="PC1", y="PC2") +
  scale_shape_manual(values=c(16,4)) + 
  geom_vline(xintercept=pc1_th, linetype="dashed", size=0.5) +
  geom_hline(yintercept=pc2_th, linetype="dashed", size=0.5) +
  myColourScale 
## Warning: Ignoring unknown aesthetics: text
ggplotly(g, tooltip="text") # By default the tooltip would also show coordinates 
## Warning in dev_fun(file = tempfile(), width = width %||% 640, height = height %||% : partial argument match of 'file' to 'filename'
# Clean-up
rm(myColours, myColourScale, plotly_group, g)

Manually select WECARE outliers

manual_outliers.df <- eigenphen_ampliseq_kgen.df %>% 
  filter(PC1 > pc1_th | PC2 > pc2_th, group=="WECARE") %>% 
  select(sample, wecare_pc_outlier)

dim(manual_outliers.df)
## [1] 37  2
manual_outliers.df
##           sample wecare_pc_outlier
## 1  133_S168_L007             FALSE
## 2  139_S123_L007             FALSE
## 3  141_S158_L007              TRUE
## 4  148_S432_L008              TRUE
## 5   16_S109_L007              TRUE
## 6  235_S535_L008             FALSE
## 7  238_S520_L008             FALSE
## 8  246_S375_L008              TRUE
## 9  256_S513_L008             FALSE
## 10  267_S48_L007             FALSE
## 11 273_S150_L007              TRUE
## 12  275_S22_L007              TRUE
## 13 277_S292_L008              TRUE
## 14 285_S374_L008             FALSE
## 15  289_S69_L007             FALSE
## 16 293_S479_L008             FALSE
## 17 308_S434_L008             FALSE
## 18 311_S137_L007              TRUE
## 19 313_S362_L008             FALSE
## 20 323_S469_L008              TRUE
## 21 326_S317_L008             FALSE
## 22 329_S373_L008              TRUE
## 23 330_S409_L008             FALSE
## 24  347_S36_L007             FALSE
## 25 352_S435_L008              TRUE
## 26 355_S365_L008              TRUE
## 27    35_S9_L007             FALSE
## 28 366_S293_L008              TRUE
## 29  368_S46_L007              TRUE
## 30 369_S230_L007             FALSE
## 31 372_S340_L008             FALSE
## 32 375_S140_L007              TRUE
## 33 385_S305_L008             FALSE
## 34 388_S259_L007             FALSE
## 35   3_S360_L008              TRUE
## 36 403_S210_L007              TRUE
## 37 408_S130_L007              TRUE
sum(manual_outliers.df$wecare_pc_outlier)
## [1] 18

Save results

save.image(paste(base_folder, "s02_explore_ethnicity_of_outliers.RData", sep="/"))

Final_section

ls()
## [1] "base_folder"                "eigenphen_ampliseq_kgen.df" "manual_outliers.df"         "pc1_th"                     "pc2_th"
sessionInfo()
## R version 3.5.1 (2018-07-02)
## Platform: x86_64-apple-darwin15.6.0 (64-bit)
## Running under: macOS  10.14.6
## 
## Matrix products: default
## BLAS: /Library/Frameworks/R.framework/Versions/3.5/Resources/lib/libRblas.0.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/3.5/Resources/lib/libRlapack.dylib
## 
## locale:
## [1] en_GB.UTF-8/en_GB.UTF-8/en_GB.UTF-8/C/en_GB.UTF-8/en_GB.UTF-8
## 
## attached base packages:
## [1] stats     graphics  grDevices utils     datasets  methods   base     
## 
## other attached packages:
## [1] plotly_4.9.0  ggplot2_3.2.0 dplyr_0.8.1   knitr_1.23   
## 
## loaded via a namespace (and not attached):
##  [1] Rcpp_1.0.1        later_0.8.0       pillar_1.4.1      compiler_3.5.1    tools_3.5.1       digest_0.6.19     jsonlite_1.6      evaluate_0.14     tibble_2.1.3      gtable_0.3.0      viridisLite_0.3.0 pkgconfig_2.0.2   rlang_0.3.4       shiny_1.3.2       crosstalk_1.0.0   yaml_2.2.0        xfun_0.7          withr_2.1.2       stringr_1.4.0     httr_1.4.0        htmlwidgets_1.3   grid_3.5.1        tidyselect_0.2.5  glue_1.3.1        data.table_1.12.2 R6_2.4.0          rmarkdown_1.13    purrr_0.3.2       tidyr_0.8.3       magrittr_1.5      promises_1.0.1    scales_1.0.0      htmltools_0.3.6   assertthat_0.2.1  xtable_1.8-4      mime_0.7          colorspace_1.4-1  httpuv_1.5.1      labeling_0.3      stringi_1.4.3     lazyeval_0.2.2    munsell_0.5.0     crayon_1.3.4
Sys.time()
## [1] "2019-08-30 18:19:48 BST"